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Table of contents
1.
Introduction
2.
Machine Learning Interview Questions for Freshers
2.1.
Question 1. What is Machine Learning?
2.2.
Question 2. Why is machine learning emerging nowadays?
2.3.
 Question 3. What are the different types of machine learning?
2.4.
 Question 4. How is supervised learning different from unsupervised learning?
2.5.
 Question 5. How do Deep learning and machine learning differ from each other?
2.6.
 Question 6. Define bias and variance. 
2.7.
 Question 7. What is overfitting? 
2.8.
 Question 8. How do you classify which algorithm is to be used to create a model?
2.9.
Question 9: What are the different methods of feature selection and feature extraction?
2.10.
Question 10: How do parametric and non-parametric machine learning algorithms differ from each other?
3.
Machine Learning Interview Questions for Intermediate
3.1.
Question 1. How is covariance different from correlation?
3.2.
Question 2: What do you understand by the Reinforcement Learning technique?
3.3.
 Question 3. What is a hypothesis in machine learning?
3.4.
Question 4: What is the tradeoff between bias and variance?
3.5.
 Question 5: When does regularization come into play in Machine Learning?
3.6.
Question 6: What is adversarial training, and how is it used in machine learning?
3.7.
Answer: Adversarial training is a technique used to train machine learning models to be robust against adversarial examples, which are input examples that are intentionally designed to cause the model to make incorrect predictions. It involves adding small perturbations to the input data during training, which helps the model learn to be less sensitive to small changes in the input.Question 7: What is regularization, and how does it work?
3.8.
Question 8: What is the curse of dimensionality, and how does it affect machine learning?
3.9.
Question 9: What is a hyperparameter, and how is it different from a parameter?
3.10.
Question 10: What is the difference between a generative adversarial network (GAN) and a variational autoencoder (VAE)?
4.
Machine Learning Interview Questions for Experienced
4.1.
Question 1: Explain the Confusion Matrix concerning Machine Learning Algorithms.
4.2.
Question 2: How is KNN different from k-means?
4.3.
Question 3. How do you handle missing or corrupted data?
4.4.
 Question 4. What are Different Kernels in SVM?
4.5.
 Question 5. Define Precision and Recall.
4.6.
Question 6. What is Linear Regression in Machine Learning?
4.7.
Question 7: What is reinforcement learning, and how is it used in machine learning?
4.8.
Question 8: What is transfer reinforcement learning, and how is it used in machine learning?
4.9.
Question 9: What is multi-task learning, and how is it used in machine learning?
4.10.
Question 10: What is a neural architecture search, and how is it used in machine learning?
5.
Conclusion
Last Updated: Jun 13, 2024
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Top Machine Learning Interview Questions & Answers (2023)

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18 Jun, 2024 @ 01:30 PM

Introduction

Are you preparing for interviews? Are you searching for a Machine Learning Engineer role? If yes, then you must prepare for Machine Learning interview questions. In this article, we will discuss Machine Learning Interview Questions.

Machine Learning Interview Questions

Machine learning is a process of training a computer program to create a statistical model based on the given data. In this article, we will discuss the basic, intermediate, and advanced levels of machine learning interview questions.   

 

Machine Learning Interview Questions for Freshers

Question 1. What is Machine Learning?

Answer. The subdivision of Artificial Intelligence that deals with system programming and automates the analysis of data to enable machines to act as a human without being explicitly programmed is Machine learning. It is the study of computer algorithms that can develop and produce desired outputs through its past learning with the help of data.


Question 2. Why is machine learning emerging nowadays?

Answer. Machine Learning is trending nowadays because it solves real-world problems. In contrast to the strict coding rules to solve any problem, its algorithms learn from the data. Later, the learnings are used to predict the feature and find insights.

 
Question 3. What are the different types of machine learning?

Answer. There are three types of machine learning. 

  • Supervised Learning: It uses labelled data to predict outcomes.
     
  • Unsupervised Learning: It uses unlabelled data to predict outcomes.
     
  • Reinforcement learning: It is trained by rewarding or punishing for the desired outcome.

 
Question 4. How is supervised learning different from unsupervised learning?

Answer. The significant difference between supervised and unsupervised learning is that, in supervised learning, the dataset is labeled and is used for classification problems. In comparison, unsupervised learning accepts unlabelled datasets to solve regression problems.

 
Question 5. How do Deep learning and machine learning differ from each other?

Answer. Machine learning is a subset of Artificial Intelligence. It focuses on machine learning itself without being explicitly programmed. On the contrary, deep learning is a subset of machine learning and focuses on how a human brain works compared to a machine.

 
Question 6. Define bias and variance

Answer. Bias is defined as the difference between the predicted value made by a model and the correct value of the model. If the bias value is high, it signifies that the prediction made by the model is inaccurate. Therefore, this value should be as reduced as possible to make the desired prediction.

Variance is defined as the number representing the prediction difference over a training set and the expected value of other training sets. A high variance may lead to a significant fluctuation in the output. Therefore, a model's production should have a low variance.

 
Question 7. What is overfitting? 

Answer. Overfitting is a concept that comes into play when the statistical data fits precisely with the training dataset. When such situation arises, the model cannot accurately perform on the unseen data. Hence, it impacts the ability and accuracy of the model. When a model is trained on the training data, it shows complete accuracy, technically a slight loss. But, there may be an error and low efficiency when using the test data.

 
Question 8. How do you classify which algorithm is to be used to create a model?

Answer. The use of machine learning algorithms is purely dependent on the type of data in the dataset. For example, if the data is linear, we'll apply linear regression. If the data represents non-linearity, the bagging algorithm will do better. If the information is to be analyzed/interpreted, we can use decision trees or SVM(Support Vector Machine). If the dataset consists of images, videos, and audio, then neural networks would be helpful to get an accurate solution. 


Question 9: What are the different methods of feature selection and feature extraction?

Answer: The different methods of feature selection include filtering methods, wrapper methods, and embedded methods. Feature extraction involves transforming the original features into a new set of features that captures the essential information in the data, using techniques like PCA(Principal Component Analysis) or SVD(Singular Value Decomposition).


Question 10: How do parametric and non-parametric machine learning algorithms differ from each other?

Answer: Parametric machine learning algorithms make assumptions about the distribution of the data and the relationship between the features and the target variable. Non-parametric algorithms do not make these assumptions and can learn more complex relationships in the data.

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Machine Learning Interview Questions for Intermediate

Question 1. How is covariance different from correlation?

Answer. Covariance is a method to measure how two variables are related to each other and how one would differ concerning the changes in the other variable. A positive value signifies a direct relationship between the variables, constraining that all other conditions remain constant and vice-versa.

Correlation is a way to mathematically represent the relationship between two random variables and has only three values; 1, 0, and -1. Here 1 denotes a positive relationship, -1 indicates a negative relationship, and 0 implies that the two variables are independent.


Question 2: What do you understand by the Reinforcement Learning technique?

Answer. Reinforcement learning consists of an agent responsible for interacting with its environment, producing actions, discovering errors, and gaining awards. Various software and machines apply this learning technique to find the appropriate behavior or path in a situation. It learns based on the reward or penalty given for every action it performs.

 
Question 3. What is a hypothesis in machine learning?

Answer. In machine learning, a Hypothesis is a method that describes the targets. It is responsible for finding the function that is the best approximation of independent features to the target. It also performs the necessary input-to-output mappings.


Question 4: What is the tradeoff between bias and variance?

Answer. Bias and variance both consist of errors. Bias represents an error because of overly simplistic assumptions. It can make the model under-fit, making it hard to have high predictive accuracy. On the other hand, variance is an error that occurs due to too much complexity in the learning algorithm. The model is prone to overfitting the training data due to significant variations in the data during the training process.

 
Question 5: When does regularization come into play in Machine Learning?

Answer: Regularization is a process in which the coefficient is regularised or shrunk toward zero in order to prevent overfitting. Regularization is necessary when the model gets overfit or underfit. It is a regression that regularizes the coefficient and estimates it towards zero. Regularization helps in reducing flexibility and restricts learning in a model to avoid the risk of overfitting.


Question 6: What is adversarial training, and how is it used in machine learning?

Answer: Adversarial training is a technique used to train machine learning models to be robust against adversarial examples, which are input examples that are intentionally designed to cause the model to make incorrect predictions. It involves adding small perturbations to the input data during training, which helps the model learn to be less sensitive to small changes in the input.

Question 7: What is regularization, and how does it work?

Answer: Regularization is a technique used in machine learning to prevent overfitting, which occurs when a model is trained too well on the training data and is unable to generalize to new, unseen data. Regularization works by adding a penalty term to the loss function during training, which encourages the model to learn simpler and more generalizable patterns in the data.


Question 8: What is the curse of dimensionality, and how does it affect machine learning?

Answer: The curse of dimensionality refers to the problem of having too many features or dimensions in the data. This can lead to overfitting and poor performance in machine learning models. This is because as the number of features increases, the amount of data needed to train the model also increases exponentially.


Question 9: What is a hyperparameter, and how is it different from a parameter?

Answer: A hyperparameter is a setting or configuration of a machine learning algorithm that is set by the user, rather than learning from the data. Examples of hyperparameters include learning rate, number of hidden layers, and regularization strength. Parameters, on the other hand, are values that are learned from the data during training, such as the weights of a neural network.


Question 10: What is the difference between a generative adversarial network (GAN) and a variational autoencoder (VAE)?

Answer: A GAN is a type of neural network that learns to generate new data that is similar to a training set, while a VAE is a generative model that learns to reconstruct input data with a low-dimensional latent representation. GANs use a discriminator network to distinguish between real and generated data, while VAEs use a probabilistic encoder and decoder to learn the latent representation of the input data.

Machine Learning Interview Questions for Experienced

Question 1: Explain the Confusion Matrix concerning Machine Learning Algorithms.

Answer: A table used to measure an algorithm's performance is a confusion matrix in machine learning. It is mainly used in supervised learning; it's called the matching matrix in unsupervised learning. The confusion matrix provides four important metrics for evaluating the performance of a classification model:

  • True Positives (TP): These are the cases where the actual label is positive, and the predicted label is also positive.
     
  • False Positives (FP): These are the cases where the actual label is negative, but the predicted label is positive.
     
  • True Negatives (TN): These are the cases where the actual label is negative, and the predicted label is also negative.
     
  • False Negatives (FN): These are the cases where the actual label is positive, but the predicted label is negative.


Using these four metrics, we can calculate various performance measures, including accuracy, precision, recall, and F1-score. For example, accuracy is the ratio of the total number of correct predictions (TP + TN) to the total number of predictions. Precision is the ratio of true positives to the total number of positive predictions (TP + FP), while recall is the ratio of true positives to the total number of actual positives (TP + FN). The F1-score is the harmonic mean of precision and recall.


Question 2: How is KNN different from k-means?

Answer: K nearest neighbors(KNN) is a subset of supervised learning algorithms used for classification purposes. In K Nearest Neighbours, a test sample is given as the class of the majority of its nearest neighbors. On the other hand, K-means is an unsupervised learning algorithm primarily used for clustering. In k-means, clustering only needs a set of unlabeled points and a threshold. The algorithm learns how to cluster the unlabelled data into groups by calculating the mean of the distance between different unlabeled points.
 

Question 3. How do you handle missing or corrupted data?

Answer. There are two ways, to handle missing data. 

  • Oversampling
     
  • Undersampling
     

Oversampling is a method that creates duplicates or new data points, while undersampling either deletes or merges the data cells. Apart from that, there are two other ways to handle a situation like this. One is to drop that column of missing or corrupted data altogether, and the other is to replace/add values in those columns. It can be done with the help of an inbuilt function in the Pandas library.

  • dropna() and isnull() functions help in finding the rows or columns with missing values and drop them.
     
  • fillna() function will replace the incorrect values with a placeholder value.  

 
Question 4. What are Different Kernels in SVM?

Answer. There are five major types of kernels in SVM:

  •  Linear kernel: It is applied when data is linearly separable. 
     
  •  Polynomial kernel: It is used when you have discrete data that has no natural notion of smoothness.
     
  • Gaussian Kernel: When there is no prior information about the data, it is used for transformation.
     
  • Radial basis kernel: It is similar to the Gaussian kernel, creating a radial decision boundary.
     
  • Sigmoid kernel: It is used as an activation function in neural networks.

 
Question 5. Define Precision and Recall.

Answer. Precision signifies the quality of the model performance, i.e., the quality of positive prediction made by the model. It answers the question, what portion of positive outputs were actually positive? It is calculated with the help of 

Formula for Precision

On the other hand, recall is the items of a particular class identified correctly. It answers the question, What portion of positive values were identified accurately? It is calculated with the help of

Formula for Recall


Question 6. What is Linear Regression in Machine Learning?

Answer. Linear Regression is a subset of the supervised Machine Learning algorithms. It is used to find a linear relationship between independent and dependent features in predictive analysis.

The equation for Linear Regression: Y= A + Bx, where:

  • X is the independent variable
     
  • Y is the dependent variable 
     
  • A is the intercept
     
  • B is the coefficient of x


Question 7: What is reinforcement learning, and how is it used in machine learning?

Answer: Reinforcement learning is a type of machine learning that involves training an agent to make decisions in an environment by maximizing a reward signal. The agent interacts with the environment by taking actions and receives rewards or penalties based on the outcomes of those actions. Reinforcement learning is used in tasks like game playing, robotics, and recommendation systems.


Question 8: What is transfer reinforcement learning, and how is it used in machine learning?

Answer: Transfer reinforcement learning is a technique used to apply knowledge learned from one reinforcement learning task to another related task. It involves transferring the policy or value function learned in the source task to the target task, which can speed up training and improve performance on tasks with limited data.


Question 9: What is multi-task learning, and how is it used in machine learning?

Answer: Multi-task learning is a technique used to train a machine learning model to perform multiple related tasks at the same time. It is used to improve the efficiency of training and the generalization performance of the model. Multi-task learning can be applied to tasks like speech recognition, object detection, and sentiment analysis.


Question 10: What is a neural architecture search, and how is it used in machine learning?

Answer: Neural architecture search is a technique used to automatically search for the optimal neural network architecture for a given task. It involves searching over a large space of possible architectures and evaluating their performance on the task. Neural architecture search can be used to improve the performance of machine learning models on tasks like image recognition and natural language processing.

Conclusion

In this article, we have discussed Machine Learning interview questions. We have discussed easy, medium, and hard Machine Learning interview questions. If you are looking for a job, you can refer to the Roles and responsibilities of a Data EngineerData Engineer at Cognizant, and Data Engineer at Apple. Refer to our guided paths on Coding Ninjas Studio to learn more about DSA, Competitive Programming, JavaScript, System Design, etc. 

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